Indoor 3D pedestrian tracking algorithm based on PDR using smarthphone

In this paper, we develop the indoor navigation system based on PDR (Pedestrian Dead Reckoning) using various sensors in smartphone. Usually PDR is consisted of step detection, step length estimation and heading estimation. The issue of PDR is step length estimation and to enhance the accuracy of step length, we apply the walking status recognition algorithm using ANN (Artificial Neuron Network). The features used in ANN are extracted through sensor signals of accelerometer and gyroscope. After recognizing the walking status, it is applied to estimate the step length. And when the status is recognized as stop, even if sensor signal is generated by redundant motion or movement of pedestrian, the moved distance is not calculated additionally and distance error is not increased. We use the barometric pressure sensor to extend the positioning area to whole building. To verify the proposed indoor navigation system, we implemented the application for android and conducted the experiment. Through the results, we demonstrated the accuracy of our system.

[1]  F. Seco,et al.  A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[2]  Chan Gook Park,et al.  New Map-Matching Algorithm Using Virtual Track for Pedestrian Dead Reckoning , 2010 .

[3]  Lawrence Wai-Choong Wong,et al.  A robust dead-reckoning pedestrian tracking system with low cost sensors , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[4]  J.W. Kim,et al.  Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors , 2007, 2007 IEEE Sensors Applications Symposium.

[5]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[6]  Youngsu Park,et al.  An efficient localization method using RFID tag floor localization and dead reckoning , 2012, 2012 12th International Conference on Control, Automation and Systems.

[7]  Jinwoo Park,et al.  A pedestrian indoor positioning system based on the Wi-Fi and walk pattering algorithm using mobile device , 2011 .

[8]  DaeEun Kim,et al.  Visual navigation using pixel intensity information , 2012, 2012 12th International Conference on Control, Automation and Systems.